Customers typically interact with customer support representatives for inquiring about a product or a service, for receiving purchase assistance, for receiving answers to queries, for raising concerns and the like. The customers and the customer support representatives may interact with each other using voice or text based interaction channels.
In some example scenarios, the customers and/or the customer support representatives, such as agents, may fill-up surveys outlining their experiences corresponding to the interactions. The data included in the surveys is typically used for predicting a level of service provisioned to the customers.
In some scenarios, a metric commonly referred to as a net experience score (NES) is predicted as a measure of quality of the customer experience afforded to the customer during an interaction between the customer and the customer support representative. Typically, the prediction of NES involves identifying if an interaction is associated with a positive sentiment or a negative sentiment based on customer/agents comments in the survey and/or analysis of interaction variables, such as customer responsiveness, overall time spent in the interaction and the like.
It is desirable to consider a range of complex emotions, such as for example, emotions like anger, sarcasm, frustration, delight, humor and the like, while predicting NES to improve an accuracy of the predicted NES.
Moreover, because interactions between the customers and the customer support representatives, nowadays, increasingly involve code-mixed content, i.e. multi-lingual content, it is desirable to predict NES while taking into account a presence of multiple languages in conversational data to predict NES with better accuracy and model customer experiences more effectively.